Application of SMOTE Technique and Optimization Algorithm Support Vector Machine in Predicting Adverse Outcome of Chronic Heart Failure
10.11783/j.issn.1002-3674.2024.06.001
- VernacularTitle:结合SMOTE技术与优化算法的支持向量机在慢性心衰不良结局预测中的应用
- Author:
Xiaotong LI
1
;
Fan CHENG
;
Jing TIAN
Author Information
1. 山西医科大学基础医学院生理学系(030001);山西医科大学细胞生理学教育部重点实验室
- Publication Type:Journal Article
- Keywords:
SMOTE;
Support vector machine;
Genetic algorithm optimization;
Particle swarm optimization;
Chronic heart failure
- From:
Chinese Journal of Health Statistics
2024;41(6):802-806
- CountryChina
- Language:Chinese
-
Abstract:
Objective Support vector machine(SVM)of optimization algorithm combined with SMOTE technique was used to predict the adverse outcome of patients with chronic heart failure and improve the prediction performance of classification model.Methods From January 2014 to December 2017,1183 inpatients diagnosed with chronic heart failure in the cardiology department of two Third-class hospitals in Shanxi Province were enrolled in this study.The medical records of the patients were collected.Construct logistic regression(LR)and support vector machine model based on the original training set,and combine SMOTE algorithm to construct LR,SVM,genetic algorithm support vector machine(GA-SVM)and particle swarm support vector machine(PSO-SVM).The classification performance of each model was comprehensively evaluated by sensitivity(SEN),accuracy(ACC),specificity(SPE),G-means,F-measure,area under receiver operating characteristic curve(AUC)and other indicators.Results Compared with classifying the original data directly,SMOTE technique was applied to equalize the data set,and the model performance was significantly improved.The results of equalization training set to construct LR,SVM,GA-SVM and PSO-SVM models show that GA-SVM and PSO-SVM are lower than LR in SPE and ACC indicators,and SEN,G-means,F-measure and AUC are better than LR.The comprehensive effect of GA-SVM and PSO-SVM is significantly higher than that of SVM(SEN,G-means and F-measure are better than SVM).Conclusion The GA-SVM or PSO-SVM model based on the equalization dataset can improve the prediction performance of SVM for the prognosis of heart failure.
